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The primary work on the ethanol pathways in GHGenius was undertaken in 1999 and while there have been some minor updates since then, the primary data in the model has not been reviewed in the past five years. There are several important issues that have been investigated and added to the model to better reflect the emissions of modern plants. These include:
1. Reviewed and updated energy requirements for grain ethanol plants. There have been significant reductions in the energy requirements of new grain ethanol plants in the past five years. This progress has been reviewed and incorporated into the model.
2. The addition of the capture and liquefaction of carbon dioxide. Ethanol plants produce a very concentrated carbon dioxide stream, which can be captured and liquefied for use in a variety of industrial applications. Alternative sources of carbon dioxide are less concentrated and require more energy to concentrate and purify. Depending on the degree to which carbon dioxide from ethanol plants displace carbon dioxide from other sources there can be an energy and emissions credit applied to the ethanol plant. This alternative processing scheme has been added to the model with the flexibility to activate or not, either fully or partially.
3. In the development of commercial ethanol from lignocellulose plants there are new co-products being developed. Some of these include fertilizers and soil conditioners. The model has been expanded to include fertilizer co-products.
4. There is some information in the literature that suggests that some animals that consume distillers dried grains have lower levels of flatulence. The literature has been surveyed for further information on this issue and this emission credit has been incorporated into the model.
A major part of any life cycle analysis is the collection of the data on the inputs and outputs of the production cycle being analyzed. The quality of the data has a large impact on the quality of the results being calculated. Data quality must balance the available time and resources against the quality of the data required to make a decision regarding overall environmental or human health impact.
In GHGenius the data on many of the production pathways is continually being updated as new information sources emerge or as processes evolve. Recently new information on the emissions of the fertilizer sector in Canada has become available, and new information on nitrous oxide emissions in the agriculture sector are also available. These changes to the model were described in a recent report on the emissions from biodiesel production ((S&T)2, 2005). These changes also impact the ethanol fuel pathways either directly in the case of fertilizer or indirectly in the case of soybean emissions since the distillers dried grains (DDG) displaces soybean meal and the emissions credit for the DDG is a function of the emissions from the soybean lifecycle.
The input variables are another class of data input. These variables can be expected to differ in different regions of the country or in different countries in response to different practices or environmental conditions. These inputs are generally found on the input sheet in GHGenius as one expects them to be changed in different circumstances.
These input variables for the different ethanol pathways have been reviewed and where better data now exists for some variables, this information has been added to GHGenius. It is important to keep in mind that the general approach to the data in GHGenius is to model the most likely scenarios, not the best-case scenario where practices or existing equipment would have to change to produce the modelled results.
Several new co-products have been added to GHGenius that are applicable to the ethanol pathways. The ability to model the capture of carbon dioxide and have that product displace gas from alternative production sources in now available. Cellulosic ethanol production processes produce large amounts of lignin, which can be processed by different means. It is possible to burn the material and produce electricity or it may be possible to sell the material for other applications. One application may be as a source of fertilizer and this capability has been added to the model. The handling of other co-products such as acetic acid has been improved in the model.
The co-product credits for DDG have been reviewed. The displacement factors for DDG have been reduced as more feed trials suggest lower displacement ratios that are used in other models and have been used in previous versions of GHGenius. The value of DDG as animal feed is a very complicated subject as there are many components in a typical ration and it is almost impossible to design a reasonably sized experiment to isolate the impact of one of the components. There is a significant amount of information on the impact of diets on methane production from cattle. It is clear that introducing dietary supplements, particularly ones with by-pass protein will reduce the methane production rate in dairy and beef cattle. Estimates of the impact of DDG on methane emission rates have been made and incorporated into the model.
Tags: Corn - Ethanol - GHGenius 2.6 - Lignocellulosic - Wheat
The original work in 2002 included an assessment of ethanol-diesel blends. Those fuels are not included in this work although a review of the emissions from ethanol production and ethanol blends is planned for the near future.
The goal of this work is to:
∑ Expand the biodiesel pathways in the model so that tallow and yellow grease pathways can be analyzed at the same time rather than have them share a pathway where the user must select which to model,
∑ Add the intermediate production of the lipid feedstock to the upstream results on Sheet K,
∑ Regionalize the production of fertilizer in the model,
∑ Review and update the data that is used in the biodiesel production pathways,
∑ Review and discuss the role of the biodiesel co-products in the LCA, and to
∑ Use the model to perform some sensitivity analysis on the inputs in the biodiesel pathways so that a better understanding of biodieselís benefits can be achieved.
GHGenius has been expanded to include five biodiesel pathways and all five are available for each model run. In addition, the upstream emissions are available for the five oils used as biodiesel feedstocks.
Tags: Biodiesel - Canola - Fertilizer - GHGenius 2.6 - Marine Oil - Soybeans - Tallow - Yellow Grease